CN112559856B - Application ordering method and device - Google Patents
Application ordering method and device Download PDFInfo
- Publication number
- CN112559856B CN112559856B CN202011419858.3A CN202011419858A CN112559856B CN 112559856 B CN112559856 B CN 112559856B CN 202011419858 A CN202011419858 A CN 202011419858A CN 112559856 B CN112559856 B CN 112559856B
- Authority
- CN
- China
- Prior art keywords
- mobile device
- application
- applications
- mobile
- equal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9538—Presentation of query results
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Telephone Function (AREA)
Abstract
The application discloses an application ordering method and device, wherein the application ordering method comprises the following steps: the method comprises the steps that the service condition of each mobile device in M mobile devices for each first application in K first applications in a cloud application store is obtained respectively; wherein M is any integer greater than or equal to 2, and K is any integer greater than or equal to 1; for the p-th mobile device, respectively calculating the group similarity weight of the p-th mobile device and each of the other (M-1) mobile devices according to the use condition; wherein p is any integer greater than or equal to 1 and less than or equal to M; and ordering the second application of the p-th mobile device according to the group similarity weight and the use condition of the second application of each mobile device in the other (M-1) mobile devices in the cloud application store. The application ranking method of the embodiment of the application performs personalized ranking on the applications of different mobile devices.
Description
Technical Field
The application relates to the technical field of mobile terminals and data processing, in particular to an application ordering method and device.
Background
With the popularization of smart phones, most networking people start to use the mobile phones to surf the internet. Whether an IOS system or an Android system, a user desires a more convenient browsing manner. Today, conventional browser access has failed to meet user needs, and a wide variety of mobile applications have evolved. This revolution is represented by smartphones and applications in smartphones, which have a strong potential for development compared to the warmth and fireless network communication protocol (WAP, wireless Application Protocol) sites.
Application stores have grown. The birth is the original purpose of enabling the smart phone user to complete more work and entertainment on the mobile phone. At the end of 09 years, the concept of a mobile phone software store is rapidly spreading, and the content in the mobile phone software store comprises mobile phone software, mobile phone games, mobile phone pictures, mobile phone topics, mobile phone ringtones, mobile phone videos and the like.
The application ordering method in the application store at present does not perform personalized ordering on the applications of different mobile devices, and does not accord with the use habits of users of different mobile devices.
Disclosure of Invention
For this reason, the application ordering method and device can perform personalized ordering for different mobile devices.
To achieve the above object, a first aspect of the present application provides an application ranking method, including:
the method comprises the steps that the service condition of each mobile device in M mobile devices for each first application in K first applications in a cloud application store is obtained respectively; wherein M is any integer greater than or equal to 2, and K is any integer greater than or equal to 1;
for the p-th mobile device, respectively calculating the group similarity weight of the p-th mobile device and each of the other (M-1) mobile devices according to the use condition; wherein p is any integer greater than or equal to 1 and less than or equal to M;
and ordering the second application of the p-th mobile device according to the group similarity weight and the use condition of the second application of each mobile device in the other (M-1) mobile devices in the cloud application store.
In some exemplary embodiments, separately calculating the group similarity weight for the p-th mobile device and each of the other (M-1) mobile devices based on usage includes:
calculating the total use condition of the p-th mobile device for the K first applications according to the use condition of the p-th mobile device for each of the K first applications;
calculating the total use condition of the ith mobile equipment for the K first applications according to the use condition of the ith mobile equipment for each of the K first applications; wherein i is any integer greater than or equal to 1 and less than or equal to M and not equal to p;
and calculating the group similarity weight of the p-th mobile device and the i-th mobile device according to the total use condition of the p-th mobile device on the K first applications and the total use condition of the i-th mobile device on the K first applications.
In some exemplary embodiments, calculating the total usage of the K first applications by the p-th mobile device based on the usage of each of the K first applications by the p-th mobile device includes:
according to the formula
Calculating the total use condition of the p-th mobile equipment on K first applications;
wherein A [ p ]]For the total usage of K first applications by the p-th mobile device,
indicating whether the (p) th mobile device is provided with the (r) th first application, wherein r is any integer which is greater than or equal to 1 and less than or equal to K, and the (r) th first application is installed on the (p) th mobile device>
The use case of the nth first application for the nth mobile device.
In some exemplary embodiments, calculating the total usage of the K first applications by the ith mobile device based on the usage of each of the K first applications by the ith mobile device includes:
according to the formula
Calculating the total use condition of the ith mobile equipment on K first applications;
wherein B [ i ]]For the total usage of K first applications by the ith mobile device,
indicating whether the (p) th mobile device is provided with the (r) th first application, wherein r is any integer which is greater than or equal to 1 and less than or equal to K, and the (r) th first application is installed on the (p) th mobile device>
The use case of the ith mobile device for the ith first application.
In some example embodiments, calculating the group similarity weight for the p-th mobile device and the i-th mobile device includes:
according to the formula
Calculating group similarity weight of the p-th mobile device and the i-th mobile device;
the group similarity weight between the p-th mobile device and the i-th mobile device is shown as the D [ i ], the total use condition of the i-th mobile device to the K first applications is shown as the B [ i ], and the total use condition of the p-th mobile device to the K first applications is shown as the A [ p ].
In some example embodiments, ranking the second applications of the p-th mobile device according to the group similarity weight and the usage of the second applications in the cloud application store by each of the other (M-1) mobile devices includes:
respectively calculating the score of each second application in the second applications of the p-th mobile device according to the group similarity weight and the use condition of each mobile device in other (M-1) mobile devices for the second applications;
the second applications are ranked according to their scores.
In some example embodiments, separately computing the score for each of the second applications of the p-th mobile device based on the group similarity weight and the usage of the second applications by each of the other (M-1) mobile devices includes:
determining whether the p-th mobile device has similarity with each of the other (M-1) mobile devices according to the group similarity weight of the p-th mobile device with each of the other (M-1) mobile devices;
calculating the score of the j second application of the p-th mobile device according to whether the p-th mobile device has similarity with each of the other (M-1) mobile devices, the group similarity weight of the p-th mobile device with each of the other (M-1) mobile devices, the use condition of each of the other (M-1) mobile devices on the j second application, and whether the j second application is installed on the p-th mobile device.
In some example embodiments, determining whether the p-th mobile device has similarity with the i-th mobile device based on the group similarity weight of the p-th mobile device with each of the other (M-1) mobile devices includes at least one of:
when the group similarity weight of the p-th mobile device and the i-th mobile device is greater than or equal to a preset threshold value, determining that the p-th mobile device and the i-th mobile device have similarity; wherein i is any integer greater than or equal to 1 and less than or equal to M and not equal to p;
and when the group similarity weight of the p-th mobile device and the i-th mobile device is smaller than a preset threshold value, determining that the p-th mobile device and the i-th mobile device have no similarity.
In some example embodiments, calculating the score for the jth second application of the jth mobile device includes:
according to the formula
Calculating a score of an ith second application in a cloud application store of the p-th mobile device;
wherein s [ j ]]Score for the jth second application in the cloud application store of the p-th mobile device, j being an integer greater than or equal to 1, dj [ i ]]For the group similarity weight of the p-th mobile device and the i-th mobile device,
for the use of the ith mobile device for the jth second application, +.>
Indicating whether the jth second application, alpha, is installed on the p-th mobile device i Indicating whether the p-th mobile device has a similarity with the i-th mobile device.
A second aspect of the present application provides an application ranking apparatus, comprising:
the acquisition module is used for respectively acquiring the service condition of each mobile device in the M mobile devices for each first application in K first applications in the cloud application store; wherein M is any integer greater than or equal to 2, and K is any integer greater than or equal to 1;
a calculation module for:
for the p-th mobile device, respectively calculating the group similarity weight of the p-th mobile device and each of the other (M-1) mobile devices according to the use condition; wherein p is any integer greater than or equal to 1 and less than or equal to M;
and ordering the second application of the p-th mobile device according to the group similarity weight and the use condition of the second application of each mobile device in the other (M-1) mobile devices in the cloud application store.
The application has the following advantages:
according to the method and the device for ordering the second application of the p-th mobile device based on the group similarity weight of the p-th mobile device and each of the other (M-1) mobile devices and the use condition of the second application of the cloud application store by the other (M-1) mobile devices, personalized ordering of the applications of different mobile devices is achieved, and the use habit of users of different mobile devices is met.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate the application and, together with the description, do not limit the application.
FIG. 1 is a flow chart of an application ranking method provided in one embodiment of the present application;
FIG. 2 is a block diagram of an application ranking apparatus according to another embodiment of the present application;
in the drawings:
201: the acquisition module 202: calculation module
Detailed Description
The following detailed description of specific embodiments of the present application refers to the accompanying drawings. It should be understood that the detailed description is presented herein for purposes of illustration and explanation only and is not intended to limit the present application.
As used in this disclosure, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
When the terms "comprises," "comprising," and/or "including" are used in this disclosure, they specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
Embodiments of the present disclosure may be described with reference to plan and/or cross-sectional views with the aid of idealized schematic diagrams of the present disclosure. Accordingly, the example illustrations may be modified in accordance with manufacturing techniques and/or tolerances.
Unless otherwise defined, all terms (including technical and scientific terms) used in this disclosure have the same meaning as commonly understood by one of ordinary skill in the art. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Fig. 1 is a flowchart of an application ranking method according to an embodiment of the present application.
As shown in fig. 1, one embodiment of the present application proposes an application ranking method, including:
In some example embodiments, the usage of the first application by the mobile device may be a number of clicks, or a length of time of use, or a number of installations, etc. of the first application by the mobile device user. What parameters are specifically used in the usage is not meant to limit the scope of the embodiments of the present application.
In some exemplary embodiments, the K first applications may be first applications that are used by most users in the application store, that is, K first applications that are used more frequently. It should be noted that the first application may be any application in the application store.
The specific value of K is not specifically limited, and the specific value of M is not specifically limited. The specific values of K and M are also not used to limit the scope of the embodiments of the present application.
In some exemplary embodiments, each of the M mobile devices may acquire the use cases of the K first applications of the mobile device, and report the use cases of the K first applications to a device that executes the application ranking method of the present application, such as a cloud server.
In some exemplary embodiments, separately calculating the group similarity weight for the p-th mobile device and each of the other (M-1) mobile devices based on usage includes:
calculating the total use condition of the p-th mobile device for the K first applications according to the use condition of the p-th mobile device for each of the K first applications;
calculating the total use condition of the ith mobile equipment for the K first applications according to the use condition of the ith mobile equipment for each of the K first applications; wherein i is any integer greater than or equal to 1 and less than or equal to M and not equal to p;
and calculating the group similarity weight of the p-th mobile device and the i-th mobile device according to the total use condition of the p-th mobile device on the K first applications and the total use condition of the i-th mobile device on the K first applications.
In some exemplary embodiments, calculating the total usage of the K first applications by the p-th mobile device based on the usage of each of the K first applications by the p-th mobile device includes:
determining the total use condition of the p-th mobile equipment on K first applications as the sum of the use conditions of the p-th mobile terminal on each first application in the first applications meeting preset conditions;
the first application meeting the preset condition comprises the following steps: and among the K first applications, the p-th mobile device is provided with the first application.
In some exemplary embodiments, calculating the total usage of the K first applications by the p-th mobile device based on the usage of each of the K first applications by the p-th mobile device includes:
according to the formula
Calculating the total use condition of the p-th mobile equipment on K first applications;
wherein A [ p ]]For the total usage of K first applications by the p-th mobile device,
indicating whether the (p) th mobile device is provided with the (r) th first application, wherein r is any integer which is greater than or equal to 1 and less than or equal to K, and the (r) th first application is installed on the (p) th mobile device>
The use case of the nth first application for the nth mobile device.
In some of the exemplary embodiments of the present invention,
indicating that the p-th mobile device has the r-th first application installed thereon,
indicating that the nth first application is not installed on the nth mobile device.
In some exemplary embodiments, calculating the total usage of the K first applications by the ith mobile device based on the usage of each of the K first applications by the ith mobile device includes:
determining the total use condition of the ith mobile equipment on K first applications as the sum of the use conditions of the ith mobile terminal on each first application in the first applications meeting preset conditions;
the first application meeting the preset condition comprises the following steps: and among the K first applications, the p-th mobile device is provided with the first application.
In some exemplary embodiments, calculating the total usage of the K first applications by the ith mobile device based on the usage of each of the K first applications by the ith mobile device includes:
according to the formula
Calculating the total use condition of the ith mobile equipment on K first applications;
wherein B [ i ]]For the total usage of K first applications by the ith mobile device,
indicating whether the (p) th mobile device is provided with the (r) th first application, wherein r is any integer which is greater than or equal to 1 and less than or equal to K, and the (r) th first application is installed on the (p) th mobile device>
The use case of the ith mobile device for the ith first application. />
In some example embodiments, calculating the group similarity weight for the p-th mobile device and the i-th mobile device includes:
and determining the group similarity weight of the p-th mobile device and the i-th mobile device as the ratio of the total use condition of the i-th mobile device to the K first applications to the total use condition of the p-th mobile device to the K first applications.
In some example embodiments, calculating the group similarity weight for the p-th mobile device and the i-th mobile device includes:
according to the formula
Calculating group similarity weight of the p-th mobile device and the i-th mobile device;
the group similarity weight between the p-th mobile device and the i-th mobile device is shown as the D [ i ], the total use condition of the i-th mobile device to the K first applications is shown as the B [ i ], and the total use condition of the p-th mobile device to the K first applications is shown as the A [ p ].
In this embodiment of the present application, the second application may be any one of the K first applications, or may be any other application other than the K first applications, which is not limited in this embodiment of the present application.
In some example embodiments, ranking the second applications of the p-th mobile device according to the group similarity weight and the usage of the second applications in the cloud application store by each of the other (M-1) mobile devices includes:
respectively calculating the score of the second application of the p-th mobile device according to the group similarity weight and the use condition of each mobile device in other (M-1) mobile devices for the second application;
the second applications are ranked according to their scores.
In some example embodiments, the second applications may be ranked in a predetermined order according to their scores.
The predetermined order is not particularly limited in the embodiment of the present application, and may be, for example, an order from large to small, an order from small to large, or the like.
In some example embodiments, separately computing the score for the second application for the p-th mobile device based on the group similarity weight and the usage of the second application by each of the other (M-1) mobile devices includes:
determining whether the p-th mobile device has similarity with each of the other (M-1) mobile devices according to the group similarity weight of the p-th mobile device with each of the other (M-1) mobile devices;
calculating the score of the j second application of the p-th mobile device according to whether the p-th mobile device has similarity with each of the other (M-1) mobile devices, the group similarity weight of the p-th mobile device with each of the other (M-1) mobile devices, the use condition of each of the other (M-1) mobile devices on the j second application, and whether the j second application is installed on the p-th mobile device.
In some example embodiments, determining whether the p-th mobile device has similarity with the i-th mobile device based on the group similarity weight of the p-th mobile device with each of the other (M-1) mobile devices includes at least one of:
when the group similarity weight of the p-th mobile device and the i-th mobile device is greater than or equal to a preset threshold value, determining that the p-th mobile device and the i-th mobile device have similarity; wherein i is any integer greater than or equal to 1 and less than or equal to M and not equal to p;
and when the group similarity weight of the p-th mobile device and the i-th mobile device is smaller than a preset threshold value, determining that the p-th mobile device and the i-th mobile device have no similarity.
In some example embodiments, calculating the score for the jth second application of the jth mobile device includes:
according to the formula
Calculating a score of an ith second application in a cloud application store of the p-th mobile device;
wherein s [ j ]]Score for the jth second application in the cloud application store of the p-th mobile device, j being an integer greater than or equal to 1, dj [ i ]]For the group similarity weight of the p-th mobile device and the i-th mobile device,
for the use of the ith mobile device for the jth second application, +.>
Indicating whether the jth second application, alpha, is installed on the p-th mobile device i Indicating whether the p-th mobile device has a similarity with the i-th mobile device.
It is noted that α i =1 indicates that the p-th mobile device has similarity to the i-th mobile deviceSex, alpha i =0 means that the p-th mobile device does not have similarity with the i-th mobile device.
According to the method and the device for ordering the second application of the p-th mobile device based on the group similarity weight of the p-th mobile device and each of the other (M-1) mobile devices and the use condition of the second application of the cloud application store by the other (M-1) mobile devices, personalized ordering of the applications of different mobile devices is achieved, and the use habit of users of different mobile devices is met.
In some exemplary embodiments, the group similarity weight of the p-th mobile device and the i-th mobile device is calculated based on the total usage of the K-th first applications by the i-th mobile device and the total usage of the K-th first applications by the p-th mobile device, so that the subsequent usage habit of the second applications by the user is more met when the score of the second applications of the p-th mobile device is calculated, and personalized ordering of the applications is further realized.
In some exemplary embodiments, the score of the j-th second application of the p-th mobile device is calculated based on the use condition of the j-th second application by the mobile device having similarity with the p-th mobile device and the group similarity weight of the mobile device having similarity with the p-th mobile device, and the second applications are ordered based on the calculated score, and the calculated score of the second application is more accordant with the use habit of the user of the p-th mobile device due to the fact that the group similarity weight is adopted as the calculation weight of the score of the second application, that is, personalized ordering of the applications is achieved.
The above steps of the methods are divided, for clarity of description, and may be combined into one step or split into multiple steps when implemented, so long as they include the same logic relationship, and they are all within the protection scope of this patent; it is within the scope of this patent to add insignificant modifications to the algorithm or flow or introduce insignificant designs, but not to alter the core design of its algorithm and flow.
Fig. 2 is a block diagram of an application ranking apparatus according to another embodiment of the present application.
As shown in fig. 2, another embodiment of the present application proposes an application device, including:
an obtaining module 201, configured to obtain a usage situation of each mobile device of the M mobile devices for each of K first applications in the cloud application store; wherein M is any integer greater than or equal to 2, and K is any integer greater than or equal to 1;
a calculation module 202 for:
for the p-th mobile device, respectively calculating the group similarity weight of the p-th mobile device and each of the other (M-1) mobile devices according to the use condition; wherein p is any integer greater than or equal to 1 and less than or equal to M;
and ordering the second application of the p-th mobile device according to the group similarity weight and the use condition of the second application of each mobile device in the other (M-1) mobile devices in the cloud application store.
In some exemplary embodiments, the calculating module 202 is specifically configured to implement calculating the group similarity weight of the p-th mobile device and each of the other (M-1) mobile devices according to the usage situation in the following manner:
calculating the total use condition of the p-th mobile device for the K first applications according to the use condition of the p-th mobile device for each of the K first applications;
calculating the total use condition of the ith mobile equipment for the K first applications according to the use condition of the ith mobile equipment for each of the K first applications; wherein i is any integer greater than or equal to 1 and less than or equal to M and not equal to p;
and calculating the group similarity weight of the p-th mobile device and the i-th mobile device according to the total use condition of the p-th mobile device on the K first applications and the total use condition of the i-th mobile device on the K first applications.
In some exemplary embodiments, the calculating module 202 is specifically configured to calculate, according to the usage of the p-th mobile device for each of the K first applications, a total usage of the K first applications by the p-th mobile device by using:
according to the formula
Calculating the total use condition of the p-th mobile equipment on K first applications;
wherein A [ p ]]For the total usage of K first applications by the p-th mobile device,
indicating whether the (p) th mobile device is provided with the (r) th first application, wherein r is any integer which is greater than or equal to 1 and less than or equal to K, and the (r) th first application is installed on the (p) th mobile device>
The use case of the nth first application for the nth mobile device.
In some exemplary embodiments, the calculating module 202 is specifically configured to calculate, according to the usage of the ith mobile device for each of the K first applications, a total usage of the K first applications by the ith mobile device by:
according to the formula
Calculating the total use condition of the ith mobile equipment on K first applications;
wherein B [ i ]]For the total usage of K first applications by the ith mobile device,
indicating whether the (p) th mobile device is provided with the (r) th first application, wherein r is any integer which is greater than or equal to 1 and less than or equal to K, and the (r) th first application is installed on the (p) th mobile device>
Enabling an ith mobile device to an ith first applicationUse case.
In some exemplary embodiments, the calculation module 202 is specifically configured to calculate the group similarity weight of the p-th mobile device and the i-th mobile device by:
according to the formula
Calculating group similarity weight of the p-th mobile device and the i-th mobile device;
the group similarity weight between the p-th mobile device and the i-th mobile device is shown as the D [ i ], the total use condition of the i-th mobile device to the K first applications is shown as the B [ i ], and the total use condition of the p-th mobile device to the K first applications is shown as the A [ p ].
In some exemplary embodiments, the computing module 202 is specifically configured to implement ranking the second applications of the p-th mobile device according to the group similarity weight and the usage of the second applications in the cloud application store by each of the other (M-1) mobile devices in the following manner:
respectively calculating the score of the second application of the p-th mobile device according to the group similarity weight and the use condition of each mobile device in other (M-1) mobile devices for the second application;
the second applications are ranked according to their scores.
In some exemplary embodiments, the calculating module 202 is specifically configured to calculate the score of the second application of the p-th mobile device according to the group similarity weight and the usage of the second application by each of the other (M-1) mobile devices by:
determining whether the p-th mobile device has similarity with each of the other (M-1) mobile devices according to the group similarity weight of the p-th mobile device with each of the other (M-1) mobile devices;
calculating the score of the j second application of the p-th mobile device according to whether the p-th mobile device has similarity with each of the other (M-1) mobile devices, the group similarity weight of the p-th mobile device with each of the other (M-1) mobile devices, the use condition of each of the other (M-1) mobile devices on the j second application, and whether the j second application is installed on the p-th mobile device.
In some exemplary embodiments, the computing module 202 is specifically configured to determine whether the p-th mobile device has a similarity with the i-th mobile device according to the group similarity weight of the p-th mobile device with each of the other (M-1) mobile devices in at least one of the following manners:
when the group similarity weight of the p-th mobile device and the i-th mobile device is greater than or equal to a preset threshold value, determining that the p-th mobile device and the i-th mobile device have similarity; wherein i is any integer greater than or equal to 1 and less than or equal to M and not equal to p;
and when the group similarity weight of the p-th mobile device and the i-th mobile device is smaller than a preset threshold value, determining that the p-th mobile device and the i-th mobile device have no similarity.
In some exemplary embodiments, the calculation module 202 is specifically configured to implement calculating the score of the jth second application of the jth mobile device in the following manner:
according to the formula
Calculating a score of an ith second application in a cloud application store of the p-th mobile device;
wherein s [ j ]]Score for the jth second application in the cloud application store of the p-th mobile device, j being an integer greater than or equal to 1, dj [ i ]]For the group similarity weight of the p-th mobile device and the i-th mobile device,
for the use of the ith mobile device for the jth second application, +.>
Representing the p-th shiftWhether the j-th second application is installed on the mobile device or not, alpha i Indicating whether the p-th mobile device has a similarity with the i-th mobile device.
The specific implementation process of the application ranking device is the same as that of the application ranking method in the foregoing embodiment, and will not be repeated here.
In this embodiment, each module is a logic module, and in practical application, one logic unit may be one physical unit, or may be a part of one physical unit, or may be implemented by a combination of a plurality of physical units. In addition, in order to highlight the innovative part of the present application, elements that are not so close to solving the technical problem presented in the present application are not introduced in the present embodiment, but it does not indicate that other elements are not present in the present embodiment.
The embodiment also provides an electronic device including one or more processors; the storage device stores one or more programs thereon, and when the one or more programs are executed by the one or more processors, the one or more processors implement the application ranking method provided in this embodiment, so that specific steps of the application ranking method are not repeated herein for avoiding repeated description.
The present embodiment also provides a computer readable medium, on which a computer program is stored, where the program when executed by a processor implements the application ranking method provided in the present embodiment, and in order to avoid repetitive description, specific steps of the application ranking method are not described herein.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the embodiments and form different embodiments.
It is to be understood that the above embodiments are merely illustrative of the exemplary embodiments employed to illustrate the principles of the present application, however, the present application is not limited thereto. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the application, and are also considered to be within the scope of the application.
Claims (8)
1. An application ordering method, comprising:
the method comprises the steps that the service condition of each mobile device in M mobile devices for each first application in K first applications in a cloud application store is obtained respectively; wherein M is any integer greater than or equal to 2, and K is any integer greater than or equal to 1;
for the p-th mobile device, respectively calculating the group similarity weight of the p-th mobile device and each of the other (M-1) mobile devices according to the use condition; wherein p is any integer greater than or equal to 1 and less than or equal to M;
ranking the second applications of the p-th mobile device according to the group similarity weight and the usage of the second applications in the cloud application store by each of the other (M-1) mobile devices;
the calculating the group similarity weight of the p-th mobile device and each of the other (M-1) mobile devices according to the use condition comprises the following steps:
calculating the total use condition of the p-th mobile device for K first applications according to the use condition of the p-th mobile device for each of the K first applications;
calculating the total use condition of the ith mobile equipment for the K first applications according to the use condition of the ith mobile equipment for each of the K first applications; wherein i is any integer greater than or equal to 1 and less than or equal to M and not equal to p;
calculating group similarity weights of the p-th mobile device and the i-th mobile device according to the total use condition of the p-th mobile device on the K first applications and the total use condition of the i-th mobile device on the K first applications;
the calculating the group similarity weight of the p-th mobile device and the i-th mobile device comprises:
according to the formula
Calculating group similarity weight of the p-th mobile device and the i-th mobile device;
and D [ i ] is the group similarity weight of the p-th mobile device and the i-th mobile device, B [ i ] is the total use condition of the i-th mobile device on K first applications, and A [ p ] is the total use condition of the p-th mobile device on K first applications.
2. The application ranking method according to claim 1, wherein the calculating the total usage of the K first applications by the p-th mobile device according to the usage of each of the K first applications by the p-th mobile device includes:
according to the formula
Calculating the total use condition of the p-th mobile equipment on K first applications;
wherein A [ p ]]For the total usage of K first applications by the p-th said mobile device,
indicating whether the p-th mobile device has the r-th first application installed thereon, r being any one integer greater than or equal to 1 and less than or equal to K,
is p-th-use case of the first application by the mobile device.
3. The application ranking method according to claim 1, wherein the calculating the total usage of the K first applications by the i-th mobile device according to the usage of the K first applications by the i-th mobile device includes:
according to the formula
Calculating the total use condition of the ith mobile equipment on K first applications;
wherein B [ i ]]For the total usage of K first applications by the ith mobile device,
indicating whether an ith of said first applications is installed on said mobile device, r being any integer greater than or equal to 1 and less than or equal to K,
and (3) the use condition of the ith mobile equipment for the ith first application is obtained.
4. The application ranking method of claim 1, wherein the ranking the second application of the p-th mobile device according to the group similarity weight and the usage of the second application in the cloud application store by each of the other (M-1) mobile devices comprises:
calculating the score of the second application of the p-th mobile device according to the group similarity weight and the use condition of the second application of each of the other (M-1) mobile devices;
and sequencing the second applications according to the scores of the second applications.
5. The application ranking method according to claim 4, wherein the calculating the score of the second application of the p-th mobile device according to the group similarity weight and the usage of the second application by each of the other (M-1) mobile devices, respectively, comprises:
determining whether the p-th mobile device has similarity with each of the other (M-1) mobile devices according to the group similarity weight of the p-th mobile device and each of the other (M-1) mobile devices;
calculating the score of the j second application of the p-th mobile device according to the condition that the p-th mobile device has similarity with each of other (M-1) mobile devices, the group similarity weight of the p-th mobile device with each of the other (M-1) mobile devices, the use condition of each of the other (M-1) mobile devices on the j-th second application, and whether the j-th second application is installed on the p-th mobile device.
6. The application ranking method of claim 5, wherein the determining whether the p-th mobile device has similarity with the i-th mobile device based on the group similarity weight of the p-th mobile device with each of the other (M-1) mobile devices comprises at least one of:
when the group similarity weight of the p-th mobile device and the i-th mobile device is greater than or equal to a preset threshold value, determining that the p-th mobile device and the i-th mobile device have similarity; wherein i is any integer greater than or equal to 1 and less than or equal to M and not equal to p;
and when the group similarity weight of the p-th mobile device and the i-th mobile device is smaller than a preset threshold value, determining that the p-th mobile device and the i-th mobile device have no similarity.
7. The application ranking method of claim 5, wherein the calculating a score for a j-th second application of a p-th mobile device comprises:
according to the formula
Calculating a score of a j-th second application in a cloud application store of a p-th mobile device;
wherein s [ j ]]A score of the jth second application in the cloud application store of the p-th mobile device, j being an integer greater than or equal to 1, di [ i ]]For the group similarity weight of the p-th mobile device and the i-th mobile device,
for the use of the j-th said second application by the i-th said mobile device,/or->
Indicating whether the jth second application, alpha, is installed on the p-th mobile device i Indicating whether the p-th mobile device has a similarity with the i-th mobile device.
8. An application ranking apparatus comprising:
the acquisition module is used for respectively acquiring the service condition of each mobile device in M mobile devices for each first application in K first applications in a cloud application store; wherein M is any integer greater than or equal to 2, and K is any integer greater than or equal to 1;
a calculation module for:
for the p-th mobile device, respectively calculating the group similarity weight of the p-th mobile device and each of the other (M-1) mobile devices according to the use condition; wherein p is any integer greater than or equal to 1 and less than or equal to M;
ranking the second applications of the p-th mobile device according to the group similarity weight and the usage of the second applications in the cloud application store by each of the other (M-1) mobile devices;
the computing module is specifically configured to implement computing, according to a usage situation, a group similarity weight of a p-th mobile device and each of other (M-1) mobile devices, by using the following manner:
calculating the total use condition of the p-th mobile device for the K first applications according to the use condition of the p-th mobile device for each of the K first applications;
calculating the total use condition of the ith mobile equipment for the K first applications according to the use condition of the ith mobile equipment for each of the K first applications; wherein i is any integer greater than or equal to 1 and less than or equal to M and not equal to p;
according to the total use condition of the p-th mobile device on the K first applications and the total use condition of the i-th mobile device on the K first applications, calculating the group similarity weight of the p-th mobile device and the i-th mobile device;
the computing module is specifically configured to implement computing a group similarity weight of the p-th mobile device and the i-th mobile device in the following manner:
according to the formula
Calculating group similarity weight of the p-th mobile device and the i-th mobile device;
the group similarity weight between the p-th mobile device and the i-th mobile device is shown as the D [ i ], the total use condition of the i-th mobile device to the K first applications is shown as the B [ i ], and the total use condition of the p-th mobile device to the K first applications is shown as the A [ p ].
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011419858.3A CN112559856B (en) | 2020-12-04 | 2020-12-04 | Application ordering method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011419858.3A CN112559856B (en) | 2020-12-04 | 2020-12-04 | Application ordering method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112559856A CN112559856A (en) | 2021-03-26 |
| CN112559856B true CN112559856B (en) | 2023-06-02 |
Family
ID=75059373
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011419858.3A Active CN112559856B (en) | 2020-12-04 | 2020-12-04 | Application ordering method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112559856B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103514239A (en) * | 2012-11-26 | 2014-01-15 | Tcl美国研究所 | Recommendation method and system integrating user behaviors and object content |
| CN104866474A (en) * | 2014-02-20 | 2015-08-26 | 阿里巴巴集团控股有限公司 | Personalized data searching method and device |
| CN108108491A (en) * | 2015-07-23 | 2018-06-01 | 海信集团有限公司 | A kind of recommendation method and device of multi-medium data |
| CN109064276A (en) * | 2018-07-25 | 2018-12-21 | 中国联合网络通信集团有限公司 | A kind of mobile terminal application sort method and system |
| CN110351318A (en) * | 2018-04-04 | 2019-10-18 | 腾讯科技（深圳）有限公司 | Using the method, terminal and computer storage medium of recommendation |
| CN111881341A (en) * | 2020-06-15 | 2020-11-03 | 合肥美的电冰箱有限公司 | Diet information recommendation method and device, electronic equipment and medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100153292A1 (en) * | 2008-12-11 | 2010-06-17 | Microsoft Corporation | Making Friend and Location Recommendations Based on Location Similarities |
| US20140019447A1 (en) * | 2011-12-18 | 2014-01-16 | Yuly Goryavskiy | Multi-attribute search system and method for ranking objects according to their attractiveness |
-
2020
- 2020-12-04 CN CN202011419858.3A patent/CN112559856B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103514239A (en) * | 2012-11-26 | 2014-01-15 | Tcl美国研究所 | Recommendation method and system integrating user behaviors and object content |
| CN104866474A (en) * | 2014-02-20 | 2015-08-26 | 阿里巴巴集团控股有限公司 | Personalized data searching method and device |
| CN108108491A (en) * | 2015-07-23 | 2018-06-01 | 海信集团有限公司 | A kind of recommendation method and device of multi-medium data |
| CN110351318A (en) * | 2018-04-04 | 2019-10-18 | 腾讯科技（深圳）有限公司 | Using the method, terminal and computer storage medium of recommendation |
| CN109064276A (en) * | 2018-07-25 | 2018-12-21 | 中国联合网络通信集团有限公司 | A kind of mobile terminal application sort method and system |
| CN111881341A (en) * | 2020-06-15 | 2020-11-03 | 合肥美的电冰箱有限公司 | Diet information recommendation method and device, electronic equipment and medium |
Non-Patent Citations (4)
| Title |
|---|
| Learning Collaborative Filtering and Its Application to People to People Recommendation in Social Networks;Xiongcai Cai 等;《2010 IEEE International Conference on Data Mining》;743-748 * |
| Liwei Liu 等.Multi-criteria service recommendation based on user criteria preferences.《RecSys '11: Proceedings of the fifth ACM conference on Recommender systems》.2011,77-84. * |
| 基于模糊理论和协同过滤的个性化推荐方法;徐丽亚;《中国优秀硕士学位论文全文数据库 信息科技辑》(第03(2017)期);I138-6148 * |
| 基于社交网络的多目标共乘群体匹配优化方法研究;蔡丹丹;《中国优秀硕士学位论文全文数据库 基础科学辑》(第01(2020)期);A002-333 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112559856A (en) | 2021-03-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20180248879A1 (en) | Method and apparatus for setting access privilege, server and storage medium | |
| US20110145234A1 (en) | Search method and system | |
| US20140108432A1 (en) | Method and apparatus of recommending popular accounts in sns system | |
| CN106951527B (en) | Song recommendation method and device | |
| CN113412608B (en) | Content pushing method and device, server and storage medium | |
| CN108540508B (en) | Method, device and equipment for pushing information | |
| WO2022007626A1 (en) | Video content recommendation method and apparatus, and computer device | |
| CN111639988A (en) | Broker recommendation method, device, electronic equipment and storage medium | |
| CN103365913A (en) | Search result ordering method and device | |
| CN105183295A (en) | Classification method for application icons and terminal | |
| CN109819002B (en) | Data pushing method and device, storage medium and electronic device | |
| CN111008059A (en) | Control method and device for popup window display, terminal and storage medium | |
| CN107562432A (en) | Information processing method and related product | |
| CN109886300A (en) | A kind of user's clustering method, device and equipment | |
| CN106294417A (en) | A kind of data reordering method, device and electronic equipment | |
| CN112559856B (en) | Application ordering method and device | |
| CN112765450A (en) | Recommended content determining method, recommended content determining device and storage medium | |
| CN112559866A (en) | College book reading recommendation method, device, equipment and storage medium | |
| CN110557351B (en) | Method and apparatus for generating information | |
| CN110457604A (en) | Recommended method and relevant device based on artificial intelligence | |
| CN112491939B (en) | Multimedia resource scheduling method and system | |
| CN111984867B (en) | Network resource determining method and device | |
| CN110674268B (en) | Man-machine conversation method and related equipment | |
| CN113449210A (en) | Personalized recommendation method and device based on space-time characteristics, electronic equipment and storage medium | |
| CN111159545A (en) | Recommended house source determining method and device and house source recommending method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |